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Joint analysis of national eco-efficiency, eco-innovation and SDGS in Europe: DEA approach

Abstract

The growing complexity and intertwining of different socio-economic issues both in individual countries and internationally mean that single-theme analyses do not consider all the relationships and thus have cognitive limitations. Therefore, studies that combine several research areas are increasingly common in the literature to clarify the connections and relationships. In this study, considering the sequential nature of the stages, a combined analysis of eco-efficiency, eco-innovation, and Sustainable Development Goals (SDGs) was performed. The analysis was carried out for 27 European Union countries in 2017–2019. Dynamic Network SBM and Dynamic Divisional Malmquist Index were used for the study. The research results show that the EU countries achieve relatively higher efficiency results in eco-innovation and SDG than ecoefficiency. The average overall efficiency level for all EU countries was only 0.63. The change in productivity was influenced by both the frontier shift and catch-up effect, but only with regard to eco-efficiency and eco-innovation. At the same time, the frontier-shift effect did not affect the change in SDG productivity.


First published online 19 October 2022

Keyword : pro-environmental technologies, eco-innovation, sustainable development, SDGs, DEA, efficiency

How to Cite
Łącka, I., & Brzezicki, Łukasz. (2022). Joint analysis of national eco-efficiency, eco-innovation and SDGS in Europe: DEA approach. Technological and Economic Development of Economy, 28(6), 1739–1767. https://doi.org/10.3846/tede.2022.17702
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References

Allen, C., Metternicht, G., & Wiedmann, T. (2018). Initial progress in implementing the Sustainable Development Goals (SDGs): A review of evidence from countries. Sustainability Science, 13, 1453–1467. https://doi.org/10.1007/s11625-018-0572-3

Allen, C., Metternicht, G., & Wiedmann, T. (2019). Prioritising SDG targets: Assessing baselines, gaps and interlinkages. Sustainability Science, 14(2), 421–438. https://doi.org/10.1007/s11625-018-0596-8

ASEM SMEs Eco-innovation Center. (2018). 2018 ASEM Eco-innovation Index. Annual report. ASEIC. Retrieved September 12, 2021, from http://aseic.org/reference/publishing.php

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078

Carayannis, E. G., Barth, T. D., & Campbell, D. F. (2012). The Quintuple Helix innovation model: Global warming as a challenge and driver for innovation. Journal of Innovation and Entrepreneurship, 1, 1–12. https://doi.org/10.1186/2192-5372-1-2

Carrillo-Hermosill, J., del Río, P., & Könnölä, T. (2010). Diversity of eco-innovations: Reflections from selected case studies. Journal of Cleaner Production, 18(10–11), 1073–1083. https://doi.org/10.1016/j.jclepro.2010.02.014

Chachuli, F. S. M., Ludin, N. A., Mat, S., & Sopian, K. (2020). Renewable energy performance evaluation studies using the data envelopment analysis (DEA): A systematic review. Journal of Renewable and Sustainable Energy, 12, 062701. https://doi.org/10.1063/5.0024750

Chang, T.-S., Tone, K., & Wu, C.-H. (2021). Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation. European Journal of Operational Research, 291(2), 766–781. https://doi.org/10.1016/j.ejor.2020.09.044

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Cheba, K., & Bąk, I. (2021). Environmental production efficiency in the European Union countries as a tool for implementation of goal 7 of the 2030 Agenda. Energies, 14(15), 4593. https://doi.org/10.3390/en14154593

Cleantech Group. (2017). The Global Cleantech Innovation Index 2017. Retrieved September 5, 2021, from https://www.cleantech.com/indexes/the-global-cleantech-innovation-index/

Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-45283-8

Ding, L.-L., Lei, L., Wang, L., & Zang, L.-F. (2020). Assessing industrial circular economy performance and its dynamic evolution: An extended Malmquist index based on cooperative game network DEA. Science of the Total Environment, 731, 139001. https://doi.org/10.1016/j.scitotenv.2020.139001

European Commission. (2019). Communication from the Commission. The European Green Deal, COM(2019) 640 final. Brussels. Retrieved September 12, 2021, from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52019DC0640

European Commission. (2020). Circular economy indicators. Retrieved September 17, 2021, from https://ec.europa.eu/environment/ecoap/indicators/circular-economy-indicators_en

European Commission. (2021). The eco-innovation scoreboard and the eco-innovation index. Retrieved September 5, 2021, from https://ec.europa.eu/environment/ecoap/indicators/index_en

Eurostat. (2021). Database. Retrieved September 5, 2021, from https://ec.europa.eu/eurostat/web/main/data/database

Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-009-1816-0

Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. https://doi.org/10.1016/S0038-0121(99)00012-9

Färe, R., & Lovell, C. A. K. (1978). Measuring the technical efficiency of production. Journal of Economic Theory, 19(1), 150–162. https://doi.org/10.1016/0022-0531(78)90060-1

Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1994). Productivity change in Swedish hospitals: A Malmquist output index approach. In A. Charnes, W. W. Cooper, A. Y. Lewin, & M. L. Seiford (Eds.), Data envelopment analysis: Theory, methodology and applications (pp. 253–272). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-011-0637-5_13

Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71(1), 90–98. https://doi.org/10.2307/1928055

Glavi, P., Lesjak, M., & Hirsbak, S. (2012, May 2–4). European training course on eco-efficiency. In 15th European Roundtable on Sustainable Consumption and Production. Bregenz, Austria. Retrieved September 12, 2021, from https://vbn.aau.dk/en/publications/european-training-course-on-eco-efficiency

Grochová, L. I., & Litzman, M. (2021). The efficiency in meeting measurable sustainable development goals. International Journal of Sustainable Development & World Ecology, 28(8), 709–719. https://doi.org/10.1080/13504509.2021.1882606

Guo, X., Lu, C. C., Lee, J. H., & Chiu, Y. H. (2017). Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134, 392–399. https://doi.org/10.1016/j.energy.2017.06.040

Jankiewicz, M., & Pietrzak, M. B. (2020). Assesment of trends in the share of expenditure on services and food in the Visegrad Group member states. International Journal of Business and Society, 21(2), 977–996. https://doi.org/10.33736/ijbs.3306.2020

Kalra, M., Panicker, D., Dixit, A., Jain, R., & Thakur, B. K. (2021). Ensuring access to sustainable and affordable energy to all. In W. Leal Filho, A. Azul, L. Brandli, A. Lange Salvia, & T. Wall (Eds.), Affordable and clean energy, encyclopedia of the UN Sustainable Development Goals (pp. 619–629). Springer. https://doi.org/10.1007/978-3-319-71057-0_139-1

Kao, C. (2014). Network data envelopment analysis: A review. European Journal of Operational Research, 239(1), 1–16. https://doi.org/10.1016/j.ejor.2014.02.039

Kiani Mavi, R., & Kiani Mavi, N. (2021). National eco-innovation analysis with big data: A common-weights model for dynamic DEA. Technological Forecasting and Social Change, 162, 120369. https://doi.org/10.1016/j.techfore.2020.120369

Kiani Mavi, R., Saen, R., & Goh, M. (2019). Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach. Technological Forecasting and Social Change, 144, 553–562. https://doi.org/10.1016/j.techfore.2018.01.035

Kemp, R., Arundel, A., Rammer, C., Miedzinski, M., Tapia, C., Barbieri, N., Tűrkeli, S., Bassi, A. M., Mazzanti, M., Chapman, D., Diaz López, F., & McDowall, W. (2019). Maastricht manual on measuring eco-innovation for a green economy. Innovation for Sustainable Development Network. Maastricht, Netherlands.

Koronakos, G. (2019). A taxonomy and review of the network data envelopment analysis literature. In G. Tsihrintzis, M. Virvou, E. Sakkopoulos, & L. Jain (Eds.), Machine learning paradigms. Learning and analytics in intelligent systems (pp. 255–311). Springer. https://doi.org/10.1007/978-3-030-15628-2_9

Kuosmanen, T. (2005). Measurement and analysis of eco-efficiency: An economist’s perspective. Journal of Industrial Ecology, 9(4), 15–18. https://doi.org/10.1162/108819805775248025

Łącka, I., & Brzezicki, Ł. (2021). The efficiency and productivity evaluation of national innovation systems in Europe. European Research Studies Journal, 24(3), 471–496. https://doi.10.35808/ersj/2440

Leal Filho, W., Fritzen, B., Ruiz Vargas, V., Paço, A., Zhang, Q., Doni, F., Azul, A. M., Vasconcelos, C. R. P., Nikolaou, I. E., Skouloudis, A., Weresa, M. A., Marczewska, M., Price, E., Anholon, R., Rampasso, I., Quelhas, O., Salvia, A. L., Ozuyar, P. G., Moggi, S., & Wu, Y. J. (2021). Social innovation for sustainable development: Assessing current trends. International Journal of Sustainable Development & World Ecology, 29(4), 311–322. https://doi.org/10.1080/13504509.2021.2013974

Li, H., Pang, S., Cao, Y., & Gao, J. (2021). Research on the evaluation of comprehensive efficiency of technological innovation and eco-environment in China. Journal of Cleaner Production, 283, 124603. https://doi.org/10.1016/j.jclepro.2020.124603

Łozowicka, A. (2020). Evaluation of the efficiency of sustainable development policy implementation in selected EU member states using DEA. The ecological dimension. Sustainability, 12(1), 435. https://doi.org/10.3390/su12010435

Madaleno, M., Moutinho, V., & Robaina, M. (2016). Economic and environmental assessment: EU cross-country. Efficiency ranking analysis. Energy Procedia, 106, 134–154. https://doi.org/10.1016/j.egypro.2016.12.111

Mardani, A., Streimikiene, D., Balezentis, T., Saman, M. Z. M., Nor, K. M., & Khoshnava, S. M. (2018). Data envelopment analysis in energy and environmental economics: An overview of the state-of-the-art and recent development trends. Energies, 11(8), 2002. https://doi.org/10.3390/en11082002

Mariz, F. B., Almeida, M. R., & Aloise, D. (2018). A review of Dynamic Data Envelopment Analysis: state of the art and applications. International Transactions in Operational Research, 25(2), 469–505. https://doi.org/10.1111/itor.12468

Miola, A., & Schiltz, F. (2019). Measuring sustainable development goals performance: How to monitor policy action in the 2030 agenda implementation? Ecological Economics, 164, 106373. https://doi.org/10.1016/j.ecolecon.2019.106373

Moutinho, V., & Madaleno, M. (2021a). A two-stage DEA model to evaluate the technical eco-efficiency indicator in the EU countries. International Journal of Environmental Research and Public Health, 18(6), 3038. https://doi.org/10.3390/ijerph18063038

Moutinho, V., & Madaleno, M. (2021b). Assessing eco-efficiency in Asian and African countries using stochastic frontier analysis. Energies, 14(4), 1168. https://doi.org/10.3390/en14041168

OECD. (2017). Green growth indicators 2017. OECD Publishing. https://doi.org/10.1787/9789264268586-en

OECD. (2021). Data. Retrieved August 12, 2021, from https://data.oecd.org/

Pais-Magalhães, V., Moutinho, V., & Marques, A. C. (2021). Scoring method of eco-efficiency using the DEA approach: Evidence from European waste sectors. Environment, Development and Sustainability, 23(7), 9726–9748. https://doi.org/10.1007/s10668-020-00709-x.

Park, M. S., Bleischewitz, R., Han, K. J., Jang, E. K., & Joo, J. H. (2017). Eco-innovation indices as tool for measuring eco-innovation. Sustainability, 9(12), 2206. https://doi.org/10.3390/su9122206

Rennings, K. (2000). Redefining innovation-Eco-innovation research and contribution from ecological economics. Ecological Economics, 32(2), 319–332. https://doi.org/10.1016/S0921-8009(99)00112-3

Sompolska-Rzechula, A., & Kurdyś-Kujawska, A. (2021). Towards understanding interactions between sustainable development goals: The role of climate-well-being linkages. Experience of EU countries. Energies, 14(7), 2025. https://doi.org/10.3390/en14072025

Stanković, J., Marjanović, I., & Stojković, N. (2021). DEA Assessment of socio-economic development of European countries. Management. Journal of Sustainable Business and Management Solutions in Emerging Economies, 26(1), 13–24. https://doi.org/10.7595/management.fon.2020.0012

Sustainable Development Solutions Network. (2021). Sustainable development report 2021. SDSN. Retrieved September 2, 2021, from https://www.sdgindex.org/reports/

Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5

Tone, K. (2004). Malmquist productivity index: Efficiency change over time. In W. W. Cooper, L. M. Seiford, & J. Zhu (Eds.), International series in operations research & management science: Vol. 71. Handbook on data envelopment analysis (pp. 203–227). Springer. https://doi.org/10.1007/1-4020-7798-X_8

Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-base measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/10.1016/j.ejor.2008.05.027

Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3–4), 145–156. https://doi.org/10.1016/j.omega.2009.07.003

Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131. https://doi.org/10.1016/j.omega.2013.04.002

Tone, K., & Tsutsui, M. (2017). The dynamic network DEA model. In K. Tone (Ed.), Advances in DEA theory and applications with extensions to forecasting models (pp. 74–84). John Wiley & Sons. https://doi.org/10.1002/9781118946688.ch9

Tsaples, G., & Papathanasiou, J. (2021). Data envelopment analysis and the concept of sustainability: A review and analysis of the literature. Renewable and Sustainable Energy Reviews, 138, 110664. https://doi.org/10.1016/j.rser.2020.110664

United Nations. (1992a). United Nations sustainable development. Agenda 21. Retrieved September 7, 2021, from https://sustainabledevelopment.un.org/outcomedocuments/agenda21

United Nations. (1992b). United Nations framework convention on climate change. FCCC/INFORMAL/84 GE.05-62220 (E) 200705. Retrieved September 10, 2021, from https://unfccc.int/resource/docs/convkp/conveng.pdf

United Nations. (2015a). Transforming our world: The 2030 agenda for sustainable development. Geneva.

United Nations. (2015b). Paris Agreement. Retrieved September 12, 2021, from https://unfccc.int/sites/default/files/english_paris_agreement.pdf

United Nations. (2015c). Indicators and a monitoring framework for the sustainable development goals. Launching a data revolution for the SDGs. Retrieved September 17, 2021, from https://resources.unsdsn.org/indicators-and-a-monitoring-framework-for-sustainable-development-goals-launching-a-data-revolution-for-the-sdgs

Vanhercke, B., Spasova, S., & Fronteddu, B. (2021). Social policy in the European Union: State of play 2020. Facing the pandemic. European Trade Union Institute (ETUI) and European Social Observatory (OSE). Retrieved September 20, 2021, from https://www.etui.org/publications/social-policy-european-union-state-play-2020

World Bank. (2021). Indicators. Retrieved August 10, 2021, from https://data.worldbank.org/indicator

World Business Council for Sustainable Development. (2006). Eco-efficiency learning module. WBCSD. Retrieved September 12, 2021, from https://www.wbcsd.org/Projects/Education/Resources/Eco-efficiency-Learning-Module

Yang, W.-C., Lu, W.-M., & Ramasamy, A. P. (2021). International environmental efficiency trends and the impact of the Paris Agreement. Energies, 14(15), 4503. https://doi.org/10.3390/en14154503

Yu, S., Liu, J., & Li, L. (2020). Evaluating provincial eco-efficiency in China: An improved network data envelopment analysis model with undesirable output. Environmental Science and Pollution Research, 27, 6886–6903. https://doi.org/10.1007/s11356-019-06958-2

Zalasiewicz, J., Waters, C., & Head, M. J. (2017). Anthropocene: Its straigraphic basis. Nature, 541, 289. https://doi.org/10.1038/541289b

Zhang, Y., Mao, Y., Jiao, L., Shuai, C., & Zhang, H. (2021). Eco-efficiency, eco-technology innovation and eco-well-being performance to improve global sustainable development. Environmental Impact Assessment Review, 89, 106580. https://doi.org/10.1016/j.eiar.2021.106580

Zhou, H., Yang, Y., Chen, Y., Zhu, J., & Shi, Y. (2021). DEA application in sustainability 1996–2019: The origins, development, and future directions. In C. Chen, Y. Chen, & V. Jayaraman (Eds.), International series in operations research & management science: Vol. 301. Pursuing sustainability. OR/MS applications in sustainable design, manufacturing, logistics, and resource management (pp. 71–109). Springer. https://doi.org/10.1007/978-3-030-58023-0_4